Virtual screening using active set dependent optimization of dissimilarity metrics
The efficiency of virtual screening in drug discovery greatly depends on three factors: (1) pharmacophore point perception (2) representation of molecular structures with a descriptor, (3) dissimilarity metric to capture matching patterns in the descriptors. In this presentation methods tackling all three key factors will be discussed.
Pharmacophore point perception relying on the calculation of the protonation state of atoms and the partial charges at a user-defined pH assigns generalized types to atoms. Topological cross-correlation of these generalized atom types provides a compact representation of pharmacophores, however, the flexibility and shape of molecular structures is poorly represented. To overcome this problem, fuzzy smoothing of descriptors is introduced.
Virtual screening calculates the dissimilarity between a pair of descriptors using various metrics. The use of metrics comprising numerous tunable parameters set by an optimization procedure can lead to 250-fold enrichment over random.
Examples and further possible applications will be discussed.
226th ACS National Meeting, New York, USA, Sept 7-11, 2003.